In [5]:
import pandas as pd
In [6]:
df = pd.read_csv("AgentLogingReport.csv")
In [7]:
df
Out[7]:
SL No Agent Date Login Time Logout Time Duration
0 1 Shivananda Sonwane 30-Jul-22 15:35:29 17:39:39 02:04:10
1 2 Khushboo Priya 30-Jul-22 15:06:59 15:07:16 00:00:17
2 3 Nandani Gupta 30-Jul-22 15:04:24 17:31:07 02:26:42
3 4 Hrisikesh Neogi 30-Jul-22 14:34:29 15:19:35 00:45:06
4 5 Mukesh 30-Jul-22 14:03:15 15:11:52 01:08:36
... ... ... ... ... ... ...
995 996 Manjunatha A 20-Jul-22 09:03:51 15:02:28 05:58:37
996 997 Bharath 20-Jul-22 09:00:49 15:01:32 06:00:43
997 998 Khushboo Priya 20-Jul-22 08:59:20 14:26:55 05:27:34
998 999 Nishtha Jain 20-Jul-22 08:43:55 14:00:33 05:16:38
999 1000 Tarun 01-Jul-22 01:52:47 12:01:07 514:08:20

1000 rows × 6 columns

In [9]:
!pip install pandas-profiling

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In [10]:
from pandas_profiling import ProfileReport
In [11]:
profile = ProfileReport(pd.read_csv('AgentLogingReport.csv'), explorative=True)
In [12]:
profile.to_file("output.html")

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In [13]:
profile
Out[13]:
























In [14]:



     


pip install sweetviz



     






















    
    






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Installing collected packages: importlib-resources, sweetviz
Successfully installed importlib-resources-5.9.0 sweetviz-2.1.4
Note: you may need to restart the kernel to use updated packages.
























In [17]:



     


import sweetviz as sv
sweet_report = sv.analyze(pd.read_csv("AgentPerformance.csv"))



     






















    
    








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In [18]:



     


sweet_report



     






















    
    
Out[18]:








<sweetviz.dataframe_report.DataframeReport at 0x1c3cd408bb0>
























In [19]:



     


sweet_report.show_html('sweet_report.html')



     






















    
    






Report sweet_report.html was generated! NOTEBOOK/COLAB USERS: the web browser MAY not pop up, regardless, the report IS saved in your notebook/colab files.
























In [20]:



     


!pip install autoviz



     






















    
    






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Building wheels for collected packages: emoji
  Building wheel for emoji (setup.py): started
  Building wheel for emoji (setup.py): finished with status 'done'
  Created wheel for emoji: filename=emoji-2.1.0-py3-none-any.whl size=212392 sha256=763bc624ffbdfa3aae8ad78eddd6ff9218d5497bdb746a834c536932ea0fcc20
  Stored in directory: c:\users\admin\appdata\local\pip\cache\wheels\b0\4a\c8\9c580a0ea94dea641b8023456a1b178a7e4509d992224ed4aa
Successfully built emoji
Installing collected packages: setuptools, panel, xgboost, wordcloud, textblob, pyamg, emoji, autoviz
  Attempting uninstall: setuptools
    Found existing installation: setuptools 61.2.0
    Uninstalling setuptools-61.2.0:
      Successfully uninstalled setuptools-61.2.0
  Attempting uninstall: panel
    Found existing installation: panel 0.13.0









    
    






ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
spyder 5.1.5 requires pyqt5<5.13, which is not installed.
spyder 5.1.5 requires pyqtwebengine<5.13, which is not installed.
conda-repo-cli 1.0.4 requires pathlib, which is not installed.
anaconda-project 0.10.2 requires ruamel-yaml, which is not installed.










    
    






    Uninstalling panel-0.13.0:
      Successfully uninstalled panel-0.13.0
Successfully installed autoviz-0.1.55 emoji-2.1.0 panel-0.12.7 pyamg-4.2.3 setuptools-60.10.0 textblob-0.17.1 wordcloud-1.8.2.2 xgboost-1.6.2
























In [21]:



     


from autoviz.AutoViz_Class import AutoViz_Class



     






















    
    






Imported v0.1.55. After importing, execute '%matplotlib inline' to display charts in Jupyter.
    AV = AutoViz_Class()
    dfte = AV.AutoViz(filename, sep=',', depVar='', dfte=None, header=0, verbose=1, lowess=False,
               chart_format='svg',max_rows_analyzed=150000,max_cols_analyzed=30, save_plot_dir=None)
Update: verbose=0 displays charts in your local Jupyter notebook.
        verbose=1 additionally provides EDA data cleaning suggestions. It also displays charts.
        verbose=2 does not display charts but saves them in AutoViz_Plots folder in local machine.
        chart_format='bokeh' displays charts in your local Jupyter notebook.
        chart_format='server' displays charts in your browser: one tab for each chart type
        chart_format='html' silently saves interactive HTML files in your local machine










    
    

















    
    

















    
    
















    
    

















    
    

















    
    












  
  


  
  




























In [22]:



     


autoviz = AutoViz_Class().AutoViz('train.csv')



     






















    
    






Shape of your Data Set loaded: (8523, 33)
#######################################################################################
######################## C L A S S I F Y I N G  V A R I A B L E S  ####################
#######################################################################################
Classifying variables in data set...










    
    









  
    

       
      Nuniques
      dtype
      Nulls
      Nullpercent
      NuniquePercent
      Value counts Min
      Data cleaning improvement suggestions
    

  
  
    

      Item_Visibility
      8325
      float64
      0
      0.000000
      97.676874
      0
      skewed column: cap or drop possible outliers
    

    

      Item_MRP
      5938
      float64
      0
      0.000000
      69.670304
      0
      
    

    

      Item_Outlet_Sales
      3493
      float64
      0
      0.000000
      40.983222
      0
      skewed column: cap or drop possible outliers
    

    

      Item_Identifier
      1559
      object
      0
      0.000000
      18.291681
      1
      combine rare categories
    

    

      Item_Weight
      555
      float64
      0
      0.000000
      6.511792
      0
      
    

    

      Outlet_Identifier
      10
      object
      0
      0.000000
      0.117330
      528
      
    

    

      Outlet_Years
      9
      int64
      0
      0.000000
      0.105597
      0
      
    

    

      Outlet_3
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Item_Type_Combined_2
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_0
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_1
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_2
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_6
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_4
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_5
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Item_Type_Combined_0
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_7
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_8
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Item_Type_Combined_1
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_Type_0
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_Type_3
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_Type_2
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_Type_1
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_Size_3
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_Size_2
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_Size_1
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_Size_0
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_Location_Type_2
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_Location_Type_1
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_Location_Type_0
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Item_Fat_Content_1
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Item_Fat_Content_0
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

    

      Outlet_9
      2
      int64
      0
      0.000000
      0.023466
      0
      
    

  












    
    






    33 Predictors classified...
        No variables removed since no ID or low-information variables found in data set
4 numeric variables in data exceeds limit, taking top 30 variables
    List of variables selected: ['Item_Weight', 'Item_Visibility', 'Item_MRP', 'Item_Outlet_Sales']
   Total columns > 30, too numerous to print.
Number of All Scatter Plots = 10










    
    






[nltk_data] Downloading collection 'popular'
[nltk_data]    | 
[nltk_data]    | Downloading package cmudict to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\cmudict.zip.
[nltk_data]    | Downloading package gazetteers to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\gazetteers.zip.
[nltk_data]    | Downloading package genesis to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\genesis.zip.
[nltk_data]    | Downloading package gutenberg to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\gutenberg.zip.
[nltk_data]    | Downloading package inaugural to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\inaugural.zip.
[nltk_data]    | Downloading package movie_reviews to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\movie_reviews.zip.
[nltk_data]    | Downloading package names to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\names.zip.
[nltk_data]    | Downloading package shakespeare to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\shakespeare.zip.
[nltk_data]    | Downloading package stopwords to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\stopwords.zip.
[nltk_data]    | Downloading package treebank to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\treebank.zip.
[nltk_data]    | Downloading package twitter_samples to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\twitter_samples.zip.
[nltk_data]    | Downloading package omw to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    | Downloading package omw-1.4 to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    | Downloading package wordnet to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    | Downloading package wordnet2021 to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    | Downloading package wordnet31 to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    | Downloading package wordnet_ic to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\wordnet_ic.zip.
[nltk_data]    | Downloading package words to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\words.zip.
[nltk_data]    | Downloading package maxent_ne_chunker to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping chunkers\maxent_ne_chunker.zip.
[nltk_data]    | Downloading package punkt to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping tokenizers\punkt.zip.
[nltk_data]    | Downloading package snowball_data to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    | Downloading package averaged_perceptron_tagger to
[nltk_data]    |     C:\Users\admin\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping taggers\averaged_perceptron_tagger.zip.
[nltk_data]    | 
[nltk_data]  Done downloading collection popular










    
    






All Plots done
Time to run AutoViz = 42 seconds 

 ###################### AUTO VISUALIZATION Completed ########################










    
    

















    
    

















    
    

















    
    

















    
    

















    
    

















    
    































In [23]:



     


!pip install dtale



     






















    
    






Collecting dtale
  Downloading dtale-2.8.1-py2.py3-none-any.whl (12.8 MB)
Collecting dash>=2.0.0
  Downloading dash-2.6.2-py3-none-any.whl (9.8 MB)
Requirement already satisfied: seaborn in d:\anaconda\lib\site-packages (from dtale) (0.11.2)
Collecting Flask-Compress
  Downloading Flask_Compress-1.13-py3-none-any.whl (7.9 kB)
Requirement already satisfied: Flask in d:\anaconda\lib\site-packages (from dtale) (1.1.2)
Collecting dash-bootstrap-components
  Downloading dash_bootstrap_components-1.2.1-py3-none-any.whl (216 kB)
Requirement already satisfied: openpyxl in d:\anaconda\lib\site-packages (from dtale) (3.0.9)
Requirement already satisfied: numpy in d:\anaconda\lib\site-packages (from dtale) (1.21.5)
Collecting dash-colorscales
  Downloading dash_colorscales-0.0.4.tar.gz (62 kB)
Collecting flask-ngrok
  Downloading flask_ngrok-0.0.25-py3-none-any.whl (3.1 kB)
Requirement already satisfied: scikit-learn in d:\anaconda\lib\site-packages (from dtale) (1.0.2)
Requirement already satisfied: plotly>=5.0.0 in d:\anaconda\lib\site-packages (from dtale) (5.6.0)
Requirement already satisfied: scipy in d:\anaconda\lib\site-packages (from dtale) (1.7.3)
Requirement already satisfied: cycler in d:\anaconda\lib\site-packages (from dtale) (0.11.0)
Requirement already satisfied: statsmodels in d:\anaconda\lib\site-packages (from dtale) (0.13.2)
Requirement already satisfied: matplotlib in d:\anaconda\lib\site-packages (from dtale) (3.5.1)
Requirement already satisfied: requests in d:\anaconda\lib\site-packages (from dtale) (2.27.1)
Collecting lz4
  Downloading lz4-4.0.2-cp39-cp39-win_amd64.whl (98 kB)
Requirement already satisfied: pandas in d:\anaconda\lib\site-packages (from dtale) (1.4.2)
Collecting dash-daq
  Downloading dash_daq-0.5.0.tar.gz (642 kB)
Requirement already satisfied: certifi in d:\anaconda\lib\site-packages (from dtale) (2021.10.8)
Requirement already satisfied: networkx in d:\anaconda\lib\site-packages (from dtale) (2.7.1)
Collecting kaleido
  Downloading kaleido-0.2.1-py2.py3-none-win_amd64.whl (65.9 MB)
Collecting missingno<=0.4.2
  Downloading missingno-0.4.2-py3-none-any.whl (9.7 kB)
Requirement already satisfied: future>=0.14.0 in d:\anaconda\lib\site-packages (from dtale) (0.18.2)
Requirement already satisfied: xlrd in d:\anaconda\lib\site-packages (from dtale) (2.0.1)
Requirement already satisfied: itsdangerous in d:\anaconda\lib\site-packages (from dtale) (2.0.1)
Collecting squarify
  Downloading squarify-0.4.3-py3-none-any.whl (4.3 kB)
Requirement already satisfied: xarray in d:\anaconda\lib\site-packages (from dtale) (0.20.1)
Collecting strsimpy
  Downloading strsimpy-0.2.1-py3-none-any.whl (45 kB)
Requirement already satisfied: et-xmlfile in d:\anaconda\lib\site-packages (from dtale) (1.1.0)
Requirement already satisfied: six in d:\anaconda\lib\site-packages (from dtale) (1.16.0)
Collecting dash-html-components==2.0.0
  Downloading dash_html_components-2.0.0-py3-none-any.whl (4.1 kB)
Collecting dash-table==5.0.0
  Downloading dash_table-5.0.0-py3-none-any.whl (3.9 kB)
Collecting dash-core-components==2.0.0
  Downloading dash_core_components-2.0.0-py3-none-any.whl (3.8 kB)
Requirement already satisfied: click>=5.1 in d:\anaconda\lib\site-packages (from Flask->dtale) (8.0.4)
Requirement already satisfied: Werkzeug>=0.15 in d:\anaconda\lib\site-packages (from Flask->dtale) (2.0.3)
Requirement already satisfied: Jinja2>=2.10.1 in d:\anaconda\lib\site-packages (from Flask->dtale) (2.11.3)
Requirement already satisfied: colorama in d:\anaconda\lib\site-packages (from click>=5.1->Flask->dtale) (0.4.4)
Requirement already satisfied: MarkupSafe>=0.23 in d:\anaconda\lib\site-packages (from Jinja2>=2.10.1->Flask->dtale) (2.0.1)
Requirement already satisfied: tenacity>=6.2.0 in d:\anaconda\lib\site-packages (from plotly>=5.0.0->dtale) (8.0.1)
Collecting brotli
  Downloading Brotli-1.0.9-cp39-cp39-win_amd64.whl (383 kB)
Requirement already satisfied: pillow>=6.2.0 in d:\anaconda\lib\site-packages (from matplotlib->dtale) (9.0.1)
Requirement already satisfied: python-dateutil>=2.7 in d:\anaconda\lib\site-packages (from matplotlib->dtale) (2.8.2)
Requirement already satisfied: pyparsing>=2.2.1 in d:\anaconda\lib\site-packages (from matplotlib->dtale) (3.0.4)
Requirement already satisfied: kiwisolver>=1.0.1 in d:\anaconda\lib\site-packages (from matplotlib->dtale) (1.3.2)
Requirement already satisfied: fonttools>=4.22.0 in d:\anaconda\lib\site-packages (from matplotlib->dtale) (4.25.0)
Requirement already satisfied: packaging>=20.0 in d:\anaconda\lib\site-packages (from matplotlib->dtale) (21.3)
Requirement already satisfied: pytz>=2020.1 in d:\anaconda\lib\site-packages (from pandas->dtale) (2021.3)
Requirement already satisfied: idna<4,>=2.5 in d:\anaconda\lib\site-packages (from requests->dtale) (3.3)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in d:\anaconda\lib\site-packages (from requests->dtale) (1.26.9)
Requirement already satisfied: charset-normalizer~=2.0.0 in d:\anaconda\lib\site-packages (from requests->dtale) (2.0.4)
Requirement already satisfied: threadpoolctl>=2.0.0 in d:\anaconda\lib\site-packages (from scikit-learn->dtale) (2.2.0)
Requirement already satisfied: joblib>=0.11 in d:\anaconda\lib\site-packages (from scikit-learn->dtale) (1.1.0)
Requirement already satisfied: patsy>=0.5.2 in d:\anaconda\lib\site-packages (from statsmodels->dtale) (0.5.2)
Building wheels for collected packages: dash-colorscales, dash-daq
  Building wheel for dash-colorscales (setup.py): started
  Building wheel for dash-colorscales (setup.py): finished with status 'done'
  Created wheel for dash-colorscales: filename=dash_colorscales-0.0.4-py3-none-any.whl size=62582 sha256=f55dc5a2d64965bd23311116b4ff49e7214ad3ae85db945905bbbcdbb777d9c8
  Stored in directory: c:\users\admin\appdata\local\pip\cache\wheels\ec\32\ec\dc8a1aa820182edf280a5e26c76fc9493ae29e196735708de2
  Building wheel for dash-daq (setup.py): started
  Building wheel for dash-daq (setup.py): finished with status 'done'
  Created wheel for dash-daq: filename=dash_daq-0.5.0-py3-none-any.whl size=669714 sha256=85c92452733869807a84834cb2f954877a720dd2a8881dacc06e1cbd25f03655
  Stored in directory: c:\users\admin\appdata\local\pip\cache\wheels\53\73\ea\8a422aab5cab848d88653ed3087c1fa05553c3fbd67fb6ab75
Successfully built dash-colorscales dash-daq
Installing collected packages: brotli, Flask-Compress, dash-table, dash-html-components, dash-core-components, dash, strsimpy, squarify, missingno, lz4, kaleido, flask-ngrok, dash-daq, dash-colorscales, dash-bootstrap-components, dtale
  Attempting uninstall: missingno
    Found existing installation: missingno 0.5.1
    Uninstalling missingno-0.5.1:
      Successfully uninstalled missingno-0.5.1
Successfully installed Flask-Compress-1.13 brotli-1.0.9 dash-2.6.2 dash-bootstrap-components-1.2.1 dash-colorscales-0.0.4 dash-core-components-2.0.0 dash-daq-0.5.0 dash-html-components-2.0.0 dash-table-5.0.0 dtale-2.8.1 flask-ngrok-0.0.25 kaleido-0.2.1 lz4-4.0.2 missingno-0.4.2 squarify-0.4.3 strsimpy-0.2.1
























In [25]:



     


import dtale



     



















In [26]:



     


dtale.show(pd.read_csv("blackFriday_train.csv"))



     






















    
    







        
        









    
    
Out[26]:
































In [ ]: